Multi-class classification assumes each input only has one ground truth label and thus often predicts a single label for an input. In contrast, in multi-label classification [1]}, [2]}, [3]}, [4]}, each input has multiple ground truth labels and thus a multi-label classifier predicts a set of labels for an input. For instance, an image could have multiple objects, attributes, or scenes. Multi-label classification has many applications such as diseases detection [5]}, object recognition [4]}, retail checkout recognition [7]}, document classification [8]}, etc..
